Constrained networks such as, for example, Low power and Lossy Networks (LLNs), e.g., sensor networks, have a myriad of applications, such as Smart Grid, Smart Cities, home and building automation, etc. Various challenges are presented with LLNs, such as lossy links, low bandwidth, battery operation, low memory and/or processing capability, etc. For instance, LLNs communicate over a physical medium that is strongly affected by environmental conditions that change over time, and often use low-cost and low-power transceiver designs with limited capabilities (e.g., low throughput and limited link margin).
One of the most critical challenges presented by LLNs is routing. Two fundamentally different routing approaches have been envisioned for LLN/ad-hoc networks: proactive and reactive routing protocols. In proactive routing protocols such as, for example, intermediate system to intermediate system (IS-IS), open shortest path first (OSPF), routing information protocol (RIP), and routing protocol for LLNs (RPL), routing topologies are pre-computed by the control plane. In reactive routing protocols such as, for example ad hoc on demand distance vector (AODV) and ad hoc on demand distance vector (LOAD), network routes are computed on-the-fly and on-demand through the use of discovery probes that are transmitted throughout the network. While both proactive and reactive routing protocols have been deployed in real-world LLNs, both types of protocols impose a relatively high level of complexity on network end nodes that may hinder network scalability.